Bria Fibo Edit

Bria Fibo Edit

Playground

Try it on WaveSpeedAI!

FIBO is an open-source JSON-native image-to-image model that maps intent to structured controls for precise enterprise image generation. Ready-to-use REST inference API, best performance, no coldstarts, affordable pricing.

Features

Bria FIBO Edit is a powerful image editing model that transforms images based on text prompts. Upload your images, describe the changes you want, and optionally provide a mask to target specific areas — the model applies intelligent edits while preserving the overall composition and quality.


Why Choose This?

  • Text-guided editing Describe edits in natural language — change objects, modify styles, transform products, and more.

  • Mask support Optionally provide a mask image to target specific areas for precise, localized edits.

  • Multi-image input Upload multiple reference images for context-aware editing.

  • Negative prompt support Specify what to avoid in the output for better control over results.

  • Structured prompts Advanced prompt formatting for complex editing instructions.


Parameters

ParameterRequiredDescription
promptYesText description of the desired edit
imagesYesSource images to edit (can add multiple)
mask_imageNoMask image to target specific areas for editing
negative_promptNoDescribe what to avoid in the output
structured_promptNoAdvanced structured prompt for complex edits
seedNoRandom seed for reproducibility (-1 for random)

How to Use

  1. Write your prompt — describe the edit you want (e.g., “Product becomes perfume”, “Change background to beach”).
  2. Upload images — add source images using ”+ Add Item” button.
  3. Add mask (optional) — upload a mask to target specific areas.
  4. Add negative prompt (optional) — specify what to avoid.
  5. Set seed — use -1 for random, or specify a number for reproducibility.
  6. Run — submit and download the edited image.

Best Use Cases

  • Product Transformation — Change product types while maintaining composition.
  • Object Replacement — Swap objects in images with text instructions.
  • Style Editing — Modify visual styles, colors, or aesthetics.
  • Background Changes — Transform backgrounds while preserving subjects.
  • Creative Retouching — Apply artistic edits with natural language.

Pro Tips

  • Be specific in your prompt — clearly describe what should change.
  • Use mask images for precise control over which areas to edit.
  • Use negative prompts to avoid unwanted elements in the output.
  • Start with simple edits to understand how the model interprets prompts.
  • Use the same seed to compare different prompts on the same image.

Notes

  • Multiple images can be uploaded for context-aware editing.
  • Mask image should be black and white — white areas will be edited.
  • For best results, use high-quality source images.

Authentication

For authentication details, please refer to the Authentication Guide.

API Endpoints

Submit Task & Query Result

set -euo pipefail

export WAVESPEED_API_KEY="your-api-key"

REQUEST_BODY=$(cat <<'JSON'
{
  "images": [
    "https://interactive-examples.mdn.mozilla.net/media/cc0-images/painted-hand-298-332.jpg"
  ],
  "seed": -1
}
JSON
)

# 1. Submit the prediction.
SUBMIT_RESPONSE=$(curl --silent --show-error --fail-with-body \
  -X POST "https://api.wavespeed.ai/api/v3/bria/fibo/edit" \
  -H "Authorization: Bearer ${WAVESPEED_API_KEY}" \
  -H "Content-Type: application/json" \
  -d "${REQUEST_BODY}")

TASK=$(printf '%s' "${SUBMIT_RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
PREDICTION_ID=$(printf '%s' "${TASK}" | jq -r '.id // empty')
if [ -z "${PREDICTION_ID}" ]; then
  printf 'Submission response did not contain a prediction id
' >&2
  exit 1
fi
RESULT_URL=$(printf '%s' "${TASK}" | jq -r '.urls.get // empty')
if [ -z "${RESULT_URL}" ]; then RESULT_URL="https://api.wavespeed.ai/api/v3/predictions/${PREDICTION_ID}/result"; fi

# 2. Poll until the prediction finishes.
while true; do
  RESPONSE=$(curl --silent --show-error --fail-with-body \
    "${RESULT_URL}" \
    -H "Authorization: Bearer ${WAVESPEED_API_KEY}")
  RESULT=$(printf '%s' "${RESPONSE}" | jq 'if type == "object" and has("data") then .data else . end')
  STATUS=$(printf '%s' "${RESULT}" | jq -r '.status // empty')

  case "${STATUS}" in
    completed) printf '%s\n' "${RESULT}" | jq '.outputs'; break ;;
    failed|cancelled|timeout) printf '%s\n' "${RESULT}" | jq . >&2; exit 1 ;;
    created|processing) sleep 2 ;;
    *) printf 'Unexpected status: %s
' "${STATUS}" >&2; exit 1 ;;
  esac
done

Parameters

Task Submission Parameters

Request Parameters

ParameterTypeRequiredDefaultRangeDescription
imagesarray<string>Yes-1 ~ 1 itemsRequired. The source image to be edited. Publicly available URL or Base64-encoded. Accepted formats: JPEG, JPG, PNG, WEBP. Must contain exactly one item.
promptstringNo-Text prompt for image generation
mask_imagestringNo-The URL of the mask image to generate an image from.
negative_promptstringNo-The negative prompt for the generation.
structured_promptstringNo--Structured prompt (JSON string). Use a structured_prompt from a previous generation's response or the /v2/structured_prompt/generate endpoint for precise refinement.
seedintegerNo-1-The random seed to use for the generation. -1 means a random seed will be used.
enable_sync_modebooleanNofalse-If set to `true`, the request attempts to wait for the generated result and return outputs in the same response. If the result is not ready within the sync wait window, the API can return a timeout body while the task continues processing. This option is only available via the API and is supported only by some models.
enable_base64_outputbooleanNofalse-If set to `true`, the prediction's `output` strings are returned as **naked base64** (no `data:<mime>;base64,` prefix). When `false` (default), outputs are returned as URLs pointing to our CDN.

Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
data.idstringUnique identifier for the prediction, Task Id
data.modelstringModel ID used for the prediction
data.outputsarrayOutput values, usually URL strings; some models return text strings or structured result objects (empty when status is not completed)
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to retrieve the prediction result
data.statusstringStatus of the task: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created (e.g., “2023-04-01T12:34:56.789Z”)
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds

Result Request Parameters

ParameterTypeRequiredDefaultDescription
idstringYes-Task ID

Result Response Parameters

ParameterTypeDescription
codeintegerHTTP status code (e.g., 200 for success)
messagestringStatus message (e.g., “success”)
dataobjectThe prediction data object containing all details
data.idstringUnique identifier for the prediction
data.modelstringModel ID used for the prediction
data.outputsarray<string | object>Array of generated outputs (empty when status is not completed). Items are usually URL strings, but may be text strings or structured result objects, depending on the model.
data.urlsobjectObject containing related API endpoints
data.urls.getstringURL to poll for the prediction result
data.statusstringStatus: created, processing, completed, or failed
data.created_atstringISO timestamp of when the request was created
data.errorstringError message (empty if no error occurred)
data.timingsobjectObject containing timing details
data.timings.inferenceintegerInference time in milliseconds
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